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Author |
Juan Ignacio Toledo; Sebastian Sudholt; Alicia Fornes; Jordi Cucurull; A. Fink; Josep Llados |
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Title |
Handwritten Word Image Categorization with Convolutional Neural Networks and Spatial Pyramid Pooling |
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Conference Article |
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2016 |
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Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) |
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10029 |
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543-552 |
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Document image analysis; Word image categorization; Convolutional neural networks; Named entity detection |
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The extraction of relevant information from historical document collections is one of the key steps in order to make these documents available for access and searches. The usual approach combines transcription and grammars in order to extract semantically meaningful entities. In this paper, we describe a new method to obtain word categories directly from non-preprocessed handwritten word images. The method can be used to directly extract information, being an alternative to the transcription. Thus it can be used as a first step in any kind of syntactical analysis. The approach is based on Convolutional Neural Networks with a Spatial Pyramid Pooling layer to deal with the different shapes of the input images. We performed the experiments on a historical marriage record dataset, obtaining promising results. |
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Merida; Mexico; December 2016 |
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Springer International Publishing |
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978-3-319-49054-0 |
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DAG; 600.097; 602.006 |
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Admin @ si @ TSF2016 |
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2877 |
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Author |
Lluis Gomez |
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Title |
Exploiting Similarity Hierarchies for Multi-script Scene Text Understanding |
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2016 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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This thesis addresses the problem of automatic scene text understanding in unconstrained conditions. In particular, we tackle the tasks of multi-language and arbitrary-oriented text detection, tracking, and script identification in natural scenes.
For this we have developed a set of generic methods that build on top of the basic observation that text has always certain key visual and structural characteristics that are independent of the language or script in which it is written. Text instances in any
language or script are always formed as groups of similar atomic parts, being them either individual characters, small stroke parts, or even whole words in the case of cursive text. This holistic (sumof-parts) and recursive perspective has lead us to explore different variants of the “segmentation and grouping” paradigm of computer vision.
Scene text detection methodologies are usually based in classification of individual regions or patches, using a priory knowledge for a given script or language. Human perception of text, on the other hand, is based on perceptual organization through which
text emerges as a perceptually significant group of atomic objects.
In this thesis, we argue that the text detection problem must be posed as the detection of meaningful groups of regions. We address the problem of text detection in natural scenes from a hierarchical perspective, making explicit use of the recursive nature of text, aiming directly to the detection of region groupings corresponding to text within a hierarchy produced by an agglomerative similarity clustering process over individual regions. We propose an optimal way to construct such an hierarchy introducing a feature space designed to produce text group hypothese with high recall and a novel stopping rule combining a discriminative classifier and a probabilistic measure of group meaningfulness based in perceptual organization. Within this generic framework, we design a text-specific object proposals algorithm that, contrary to existing generic object proposals methods, aims directly to the detection of text regions groupings. For this, we abandon the rigid definition of “what is text” of traditional specialized text detectors, and move towards more fuzzy perspective of grouping-based object proposals methods.
Then, we present a hybrid algorithm for detection and tracking of scene text where the notion of region groupings plays also a central role. By leveraging the structural arrangement of text group components between consecutive frames we can improve
the overall tracking performance of the system.
Finally, since our generic detection framework is inherently designed for multi-language environments, we focus on the problem of script identification in order to build a multi-language end-toend reading system. Facing this problem with state of the art CNN classifiers is not straightforward, as they fail to address a key
characteristic of scene text instances: their extremely variable aspect ratio. Instead of resizing input images to a fixed size as in the typical use of holistic CNN classifiers, we propose a patch-based classification framework in order to preserve discriminative parts of the image that are characteristic of its class. We describe a novel method based on the use of ensembles of conjoined networks to jointly learn discriminative stroke-parts representations and their relative importance in a patch-based classification scheme. |
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Ph.D. thesis |
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Dimosthenis Karatzas |
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DAG |
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Admin @ si @ Gom2016 |
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2891 |
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Author |
Lluis Gomez; Dimosthenis Karatzas |
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Title |
A fast hierarchical method for multi‐script and arbitrary oriented scene text extraction |
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Journal Article |
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Year |
2016 |
Publication |
International Journal on Document Analysis and Recognition |
Abbreviated Journal |
IJDAR |
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19 |
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4 |
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335-349 |
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Keywords |
scene text; segmentation; detection; hierarchical grouping; perceptual organisation |
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Typography and layout lead to the hierarchical organisation of text in words, text lines, paragraphs. This inherent structure is a key property of text in any script and language, which has nonetheless been minimally leveraged by existing text detection methods. This paper addresses the problem of text
segmentation in natural scenes from a hierarchical perspective.
Contrary to existing methods, we make explicit use of text structure, aiming directly to the detection of region groupings corresponding to text within a hierarchy produced by an agglomerative similarity clustering process over individual regions. We propose an optimal way to construct such an hierarchy introducing a feature space designed to produce text group hypotheses with
high recall and a novel stopping rule combining a discriminative classifier and a probabilistic measure of group meaningfulness based in perceptual organization. Results obtained over four standard datasets, covering text in variable orientations and different languages, demonstrate that our algorithm, while being trained in a single mixed dataset, outperforms state of the art
methods in unconstrained scenarios. |
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DAG; 600.056; 601.197 |
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Admin @ si @ GoK2016a |
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2862 |
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Author |
Lluis Gomez; Dimosthenis Karatzas |
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Title |
A fine-grained approach to scene text script identification |
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Conference Article |
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Year |
2016 |
Publication |
12th IAPR Workshop on Document Analysis Systems |
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192-197 |
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This paper focuses on the problem of script identification in unconstrained scenarios. Script identification is an important prerequisite to recognition, and an indispensable condition for automatic text understanding systems designed for multi-language environments. Although widely studied for document images and handwritten documents, it remains an almost unexplored territory for scene text images. We detail a novel method for script identification in natural images that combines convolutional features and the Naive-Bayes Nearest Neighbor classifier. The proposed framework efficiently exploits the discriminative power of small stroke-parts, in a fine-grained classification framework. In addition, we propose a new public benchmark dataset for the evaluation of joint text detection and script identification in natural scenes. Experiments done in this new dataset demonstrate that the proposed method yields state of the art results, while it generalizes well to different datasets and variable number of scripts. The evidence provided shows that multi-lingual scene text recognition in the wild is a viable proposition. Source code of the proposed method is made available online. |
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Santorini; Grecia; April 2016 |
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DAG; 601.197; 600.084 |
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Admin @ si @ GoK2016b |
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2863 |
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Author |
Marçal Rusiñol; J. Chazalon; Jean-Marc Ogier |
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Title |
Filtrage de descripteurs locaux pour l'amélioration de la détection de documents |
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Conference Article |
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Year |
2016 |
Publication |
Colloque International Francophone sur l'Écrit et le Document |
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Local descriptors; mobile capture; document matching; keypoint selection |
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In this paper we propose an effective method aimed at reducing the amount of local descriptors to be indexed in a document matching framework.In an off-line training stage, the matching between the model document and incoming images is computed retaining the local descriptors from the model that steadily produce good matches. We have evaluated this approach by using the ICDAR2015 SmartDOC dataset containing near 25000 images from documents to be captured by a mobile device. We have tested the performance of this filtering step by using ORB and SIFT local detectors and descriptors. The results show an important gain both in quality of the final matching as well as in time and space requirements. |
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Toulouse; France; March 2016 |
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CIFED |
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DAG; 600.084; 600.077 |
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Admin @ si @ RCO2016 |
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2755 |
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Author |
Marc Sunset Perez; Marc Comino Trinidad; Dimosthenis Karatzas; Antonio Chica Calaf; Pere Pau Vazquez Alcocer |
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Title |
Development of general‐purpose projection‐based augmented reality systems |
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2016 |
Publication |
IADIs international journal on computer science and information systems |
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IADIs |
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11 |
Issue |
2 |
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1-18 |
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Despite the large amount of methods and applications of augmented reality, there is little homogenizatio n on the software platforms that support them. An exception may be the low level control software that is provided by some high profile vendors such as Qualcomm and Metaio. However, these provide fine grain modules for e.g. element tracking. We are more co ncerned on the application framework, that includes the control of the devices working together for the development of the AR experience. In this paper we describe the development of a software framework for AR setups. We concentrate on the modular design of the framework, but also on some hard problems such as the calibration stage, crucial for projection – based AR. The developed framework is suitable and has been tested in AR applications using camera – projector pairs, for both fixed and nomadic setups |
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DAG; 600.084 |
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Admin @ si @ SCK2016 |
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2890 |
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Author |
Oriol Vicente; Alicia Fornes; Ramon Valdes |
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Title |
The Digital Humanities Network of the UABCie: a smart structure of research and social transference for the digital humanities |
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2016 |
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Digital Humanities Centres: Experiences and Perspectives |
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Warsaw; Poland; December 2016 |
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DHLABS |
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DAG; 600.097 |
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Admin @ si @ VFV2016 |
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2908 |
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Q. Bao; Marçal Rusiñol; M.Coustaty; Muhammad Muzzamil Luqman; C.D. Tran; Jean-Marc Ogier |
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Title |
Delaunay triangulation-based features for Camera-based document image retrieval system |
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Conference Article |
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2016 |
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12th IAPR Workshop on Document Analysis Systems |
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1-6 |
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Camera-based Document Image Retrieval; Delaunay Triangulation; Feature descriptors; Indexing |
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In this paper, we propose a new feature vector, named DElaunay TRIangulation-based Features (DETRIF), for real-time camera-based document image retrieval. DETRIF is computed based on the geometrical constraints from each pair of adjacency triangles in delaunay triangulation which is constructed from centroids of connected components. Besides, we employ a hashing-based indexing system in order to evaluate the performance of DETRIF and to compare it with other systems such as LLAH and SRIF. The experimentation is carried out on two datasets comprising of 400 heterogeneous-content complex linguistic map images (huge size, 9800 X 11768 pixels resolution)and 700 textual document images. |
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Santorini; Greece; April 2016 |
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DAG; 600.061; 600.084; 600.077 |
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Admin @ si @ BRC2016 |
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2757 |
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Author |
Sounak Dey; Anguelos Nicolaou; Josep Llados; Umapada Pal |
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Title |
Local Binary Pattern for Word Spotting in Handwritten Historical Document |
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Conference Article |
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2016 |
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Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) |
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574-583 |
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Local binary patterns; Spatial sampling; Learning-free; Word spotting; Handwritten; Historical document analysis; Large-scale data |
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Digital libraries store images which can be highly degraded and to index this kind of images we resort to word spotting as our information retrieval system. Information retrieval for handwritten document images is more challenging due to the difficulties in complex layout analysis, large variations of writing styles, and degradation or low quality of historical manuscripts. This paper presents a simple innovative learning-free method for word spotting from large scale historical documents combining Local Binary Pattern (LBP) and spatial sampling. This method offers three advantages: firstly, it operates in completely learning free paradigm which is very different from unsupervised learning methods, secondly, the computational time is significantly low because of the LBP features, which are very fast to compute, and thirdly, the method can be used in scenarios where annotations are not available. Finally, we compare the results of our proposed retrieval method with other methods in the literature and we obtain the best results in the learning free paradigm. |
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Merida; Mexico; December 2016 |
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S+SSPR |
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DAG; 600.097; 602.006; 603.053 |
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Admin @ si @ DNL2016 |
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2876 |
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Author |
Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades |
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Sparse representation over learned dictionary for symbol recognition |
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2016 |
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Signal Processing |
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125 |
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36-47 |
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Symbol Recognition; Sparse Representation; Learned Dictionary; Shape Context; Interest Points |
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In this paper we propose an original sparse vector model for symbol retrieval task. More speci cally, we apply the K-SVD algorithm for learning a visual dictionary based on symbol descriptors locally computed around interest points. Results on benchmark datasets show that the obtained sparse representation is competitive related to state-of-the-art methods. Moreover, our sparse representation is invariant to rotation and scale transforms and also robust to degraded images and distorted symbols. Thereby, the learned visual dictionary is able to represent instances of unseen classes of symbols. |
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DAG; 600.061; 600.077 |
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Admin @ si @ DTR2016 |
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2946 |
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